Another well known technique to bulk out the training data in image recognition is to zoom-in and/or -out the training image [1]}.
We here borrow the zoom-in/out concept for the fluid flow estimation.
In this study, for the demonstration of super-resolution analysis, we combine the concept of zoom-in augmentation to transfer learning (or fine tuning), which has also been known as a good candidate to ease the training process by setting proper initial weights [2]}.
To prepare the zoomed-in image, we simply divide the original training data into several sub-domains as shown in figure REF .
